Design and characterization of flattening filter for high dose rate 192Ir and 60Co Leipzig applicators used in skin cancer brachytherapy: A Monte Carlo study

75Se - A promising alternative to 192Ir for potential use in the skin cancer brachytherapy: A Monte Carlo simulation study using FLUKA code

This study aimed to evaluate the possibility of utilizing the HDR ⁷⁵Se source for skin cancer brachytherapy. In this work, based on the BVH-20 skin applicator, two cup-shaped applicators, without and with the flattening filter, were modeled. To obtain the optimal flattening filter shape, an approach based on the MC simulation in combination with an analytical estimation was used. Then, the dose distributions for ⁷⁵Se-applicators were generated using MC simulations in water, and their dosimetric characterizations such as flatness, symmetry, and penumbra were evaluated. Furthermore, the radiation leakage in the backside of the applicators was estimated by additional MC simulation. Finally, to evaluate the treatment times, calculations were performed for two ⁷⁵Se-applicators assuming 5 Gy per fraction. The flatness, symmetry, and penumbra values for the ⁷⁵Se-applicator without the flattening filter were estimated to be 13.7%, 1.05, and 0.41 cm respectively. The corresponding values for ⁷⁵Se-applicator with the flattening filter were estimated to be 1.6%, 1.06, and 0.10 cm respectively. The radiation leakage value at a distance of 2 cm from the applicator surface was calculated to be 0.2% and 0.4% for the ⁷⁵Se-applicator without and with the flattening filter respectively. Our results showed that the treatment time for the ⁷⁵Se-applicator is comparable with that of the ¹⁹²Ir-Leipzig applicator. The findings revealed that the dosimetric parameters of the ⁷⁵Se applicator are comparable with the ¹⁹²Ir skin applicator. Overall, the ⁷⁵Se source can be an alternative to ¹⁹²Ir sources for HDR brachytherapy of skin cancer.

Investigation of the effects of biomaterials on proton Bragg peak and secondary neutron production by the Monte Carlo method in the slab head phantom

Interest in proton therapy has increased in the last decade, as protons are effective to treat deeply located tumors, cause less damage to healthy tissue and allow controlling the energy to be transferred in a target-oriented manner (or energy transfer within target limits). It is known that secondary particles such as neutrons are produced by a result of nuclear interactions of protons with the target. Secondary neutrons can cause an uncontrolled dose increase in healthy tissue near the target site, and because they have a high radiobiological effectiveness, they raise the risk of secondary cancer. There are not enough studies examining the effect of biomaterials on secondary neutron production (SNP) in proton therapy. This study aims to investigate the effect of biomaterials used as implants instead of cranium in the skull on proton depth dose distribution and SNP with Monte Carlo-based PHITS code. Therefore, Bragg peaks and SNPs for 40-140 MeV energy protons were calculated and compared with the literature in a slab head phantom containing stainless steel, CoCrMo (CCM) alloy, alumina, polytetrafluoroethylene, Ti alloy, and NiTi alloy biomaterials used in cranioplasty. It was observed that the most compatible biomaterial compared to cranium for all energies is polytetrafluoroethylene. When polytetrafluoroethylene biomaterial was placed instead of the cranium in the skull, the Bragg peak position of the 100 MeV protons was decreased by 5.04% compared to that in the cranium. In this case, the energy absorbed in the polytetrafluoroethylene biomaterial increased by approximately 28% compared to the cranium, while it decreased by approx. 4% in the brain tissue. It was also observed that while SNP was 0.0501 in the cranium, it increased by almost 18% in PTFE.

Evaluation of bone dose arising from skin cancer brachytherapy: A comparison between 192Ir and 60Co sources through Monte Carlo simulations

PURPOSE

This study aimed to calculate and compare absorbed dose to bone following exposure to ¹⁹²Ir and ⁶⁰Co sources in high dose rate (HDR) skin brachytherapy. Moreover, effects of the bone thickness and soft tissue thickness before the bone on absorbed dose to the bone are evaluated .

MATERIALS AND METHODS

¹⁹²Ir and ⁶⁰Co sources inserted in Leipzig applicators with internal diameters of 1, 2 and 3 cm with/without their optimal flattening filters were simulated by MCNPX Monte Carlo code. Then, heterogeneous phantoms (including skin, soft tissue before and after the bone, cortical bone and bone marrow) were defined. Finally, relative depth dose values for the bone and other tissues in the heterogeneous phantoms were obtained and compared.

RESULTS

The average relative depth dose values of the skin, soft tissue before and after bone and bone marrow were almost similar for both ¹⁹²Ir and ⁶⁰Co sources, with a maximum difference less than 2%. However, a 0.1-6.8% difference was observed between average relative depth dose values of these two sources for the cortical bone. The results showed that with increasing the bone thickness and bone distance from the skin surface, the average relative depth dose values of the bone marrow and cortical bone decreased for both ¹⁹²Ir and ⁶⁰Co sources inserted in the applicators without/with their optimal flattening filters. For most of evaluated the applicators without/with their flattening filters, the average relative depth dose values of the bone marrow arisen from the ⁶⁰Co source were higher than those obtained from the ¹⁹²Ir source, while an opposite trend was observed for the cortical bone .

CONCLUSION

The obtained findings showed that the average relative depth dose values of ¹⁹²Ir and ⁶⁰Co sources at the corresponding depths of the designed heterogeneous phantoms were almost similar (expect for the cortical bone). Hence, it is concluded that ⁶⁰Co source can be used instead of ¹⁹²Ir source in HDR skin brachytherapy, particularly in developing countries.

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020)

Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.